Reviews: Adaptive Classification for Prediction Under a Budget

The paper proposes a framework to train a composite classifier under a test-time budget for feature construction. The classifier consists of a high-performance model, built with no constraints on cost, a low-prediction cost model and a gating function which selects one of the two models to be used for a test sample. The setup of the problem is fairly specific: there are no constraints on complexity or feature computation cost during training time, however, at test time the prediction should be made as well as possible using as few features as possible (with a tradeoff). While several domain where this setup is relevant are mentioned in the intro, no details are given on the intended application until the experiment section. Also, only for one of the datasets on which this was tested (the Yahoo one) is runtime prediction even a consideration.